Data visualization plays an important role in machine learning. Visualizations that are directly related to the above key things in machine learning are called machine learning visualizations. Creating machine learning visualizations is sometimes a complicated process as it requires a lot of code to write even in Python. But, thanks to Python's open-source Yellowbrick library, even complex machine learning visualizations can be created with less code. That library extends the Scikit-learn API and provides high-level functions for visual diagnostics that are not provided by Scikit-learn.

Data visualization plays an important role in machine learning. Visualizations that are directly related to the above key things in machine learning are called machine learning visualizations. Creating machine learning visualizations is sometimes a complicated process as it requires a lot of code to write even in Python. But, thanks to Python's open-source Yellowbrick library, even complex machine learning visualizations can be created with less code. That library extends the Scikit-learn API and provides high-level functions for visual diagnostics that are not provided by Scikit-learn.

An important step in developing machine learning models is to evaluate the performance. Depending on the type of machine learning problem that you are dealing with, there is generally a choice of metrics to choose from to perform this step. However, simply looking at one or two numbers in isolation cannot always enable us to make the right choice for model selection. For example, a single error metric doesn't give us any information about the distribution of the errors. It does not answer questions like is the model wrong in a big way a small number of times, or is it producing lots of smaller errors?

You can find more info and examples on the Github repo linked above. Magnitude is "a fast, simple vector embedding utility library." A feature-packed Python package and vector storage file format for utilizing vector embeddings in machine learning models in a fast, efficient, and simple manner developed by Plasticity. It is primarily intended to be a simpler / faster alternative to Gensim, but can be used as a generic key-vector store for domains outside NLP. The repo provides links to a variety of popular embedding models which have been prepared in the .magnitude

Yellowbrick is a suite of visual diagnostic tools called "Visualizers" that extend the Scikit-Learn API to allow human steering of the model selection process. In a nutshell, Yellowbrick combines scikit-learn with matplotlib in the best tradition of the scikit-learn documentation, but to produce visualizations for your models! For more on Yellowbrick, please see the About. If you're new to Yellowbrick, checkout the Quick Start or skip ahead to the Model Selection Tutorial. Yellowbrick is a rich library with many Visualizers being added on a regular basis.